Weighted kernel model for text categorization

Traditional bag-of-words model and recent wordsequence kernel are two well-known techniques in the field of text categorization. Bag-of-words representation neglects the word order, which could result in less computation accuracy for some types of documents. Word-sequence kernel takes into account word order, but does not include all information of the word frequency. A weighted kernel model that combines these two models was proposed by the authors [1]. This paper is focused on the optimization of the weighting parameters, which are functions of word frequency. Experiments have been conducted with Reuter's database and show that the new weighted kernel achieves better classification accuracy. © 2006, Australian Computer Society, Inc

Weighted kernel model for text categorization

Traditional bag-of-words model and recent word-sequence kernel are two well-known techniques in the field of text categorization. Bag-of-words representation neglects the word order, which could result in less computation accuracy for some types of documents. Word-sequence kernel takes into account word order, but does not include all information of the word frequency. A weighted kernel model that combines these two models was proposed by the authors. This paper is focused on the optimization of the weighting parameters, which are functions of word frequency. Experiments have been conducted with Reuter’s database and show that the new weighted kernel achieves better classification accuracy